This section contains interactive Jupyter notebooks used throughout the course. Each notebook provides multiple ways to access the content:

• 📓 Embedded Preview: View the complete notebook rendered directly on this site
• 📥 Download: Get the .ipynb file to run locally with Jupyter
• 👁️ View on GitHub: Clean display using GitHub’s native notebook viewer
• 🚀 Open in Colab: Run immediately in Google Colab (no local setup required)

Available Notebooks

Module 1: Foundations

• Classification Performance - Metrics and evaluation techniques for classification models

Module 2: Linear Methods

• GLM - Poisson and Logistic - Generalized Linear Models with practical examples
• Ridge and Lasso Regression - Regularized regression techniques
• Linear Regression - Linear regression fundamentals and implementation
• Logistic Regression - Binary and multiclass classification

Getting Started

Running Locally

1. Download any notebook using the download button
2. Install Jupyter: pip install jupyter notebook
3. Start Jupyter: jupyter notebook
4. Navigate to and open the downloaded file

Using Google Colab

1. Click the “🚀 Open in Colab” button on any notebook page
2. Sign in with your Google account
3. Run cells directly in your browser
4. Save copies to your Google Drive

Required Packages

Most notebooks require these Python packages:

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

For specific requirements, check the first cell of each notebook.

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Table of contents

• GLM - Poisson and Logistic
• Ridge and Lasso Regression
• Classification Performance
• Linear Regression
• Logistic Regression
• Regression (ANN)
• Linear Methods - Applications
• ANN Grid Search and Regularization
• XOR demonstration
• CNN MNIST Example using Keras
• RNN Examples
• Local Regression
• CNN Pytorch
• PCA
• Comparison of Dimensionality Reduction Methods